Discover gists

## README.md

      
              2 files
            
          
              2 forks
            
          
                4 comments
              
            
              41 stars
            
          
                XDflight
                / README.md
            
            
              Last active
              July 1, 2026 05:48
            
              
                Remove cache and old extensions under "~/.vscode-server/" on your Linux server
              
          
    If you often connect to your Linux server using VSCode, the "~/.vscode-server/" folder (and sometimes the ~/.cache/ folder too) can get very large because VSCode:

Does NOT clean its download cache after installing extensions;
Does NOT delete old extensions after updating them;
Does NOT remove old VSCode servers after installing a new version.

If your server storage space is limited, you might consider cleaning "~/.vscode-server/" (and ~/.cache/) regularly using the bash script I wrote.
Simply run the following command:
curl -sL https://gist.githubusercontent.com/XDflight/5f3509eb84fc282b88059c909036f5bc/raw/fe2a3e7dab19160c17efc7025d138f5350964c0f/clean_vscode-server.sh | bash -s


## llm-wiki.md

      
              1 file
            
          
              8412 forks
            
          
                941 comments
              
            
              40343 stars
            
          
                karpathy
                / llm-wiki.md
            
            
              Created
              April 4, 2026 16:25
            
              
                llm-wiki
              
          
    LLM Wiki

A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

  
## deploy-mern.md

      
              1 file
            
          
              17 forks
            
          
                16 comments
              
            
              56 stars
            
          
                rmiyazaki6499
                / deploy-mern.md
            
            
              Last active
              July 1, 2026 05:44
            
              
                Deploying a Production ready React-Express app on AWS EC2 with CI/CD
              
          
    Deploying a Production ready React-Express app on AWS

In this tutorial, I will be going over to how to deploy a Javascript app from start to finish using AWS and EC2. Recently, my partner Tu and I launched our app AlgoAcademy (a resource for reviewing algorithms and data structures) and we wanted to share with other developers some of the lessons we learned along the way.
Following this tutorial, you will have an application that has:

A React frontend, Express backend
An AWS EC2 server configured to host your application
SSL-certification with Certbot
A custom domain name


## ParticleController.cs
using UnityEngine;

public class ParticleController : MonoBehaviour {
    [SerializeField] ComputeShader compute;
    [SerializeField] Mesh mesh;
    [SerializeField] Material material;
    [SerializeField] int particleCount = 10000;
    [SerializeField] float areaSize = 10f;
    [SerializeField] float spawnDistanceFromCamera = 8f;

## raspberry-pi-2-3-and-4-wireless-bridge-ubuntu-server-18.04-arm+netplan.md

      
              2 files
            
          
              12 forks
            
          
                11 comments
              
            
              57 stars
            
          
                Jiab77
                / raspberry-pi-2-3-and-4-wireless-bridge-ubuntu-server-18.04-arm+netplan.md
            
            
              Last active
              July 1, 2026 05:43
            
              
                The initial goal of this project is to use the raspberry pi in place of my Wireless Range Extender and then going from 3 wireless networks (2.4ghz, 2.4ghz extended, 5ghz) at home to only one (5ghz). This way I'm reducing the exposition to radio waves for the whole familly at home.
              
          
    Raspberry Pi 2/3B/B+/4B Wireless Bridge using Ubuntu Server 18.04 ARM Image and Netplan

The initial goal of this project is to use the raspberry pi in place of my Wireless Range Extender and then going from 3 wireless
networks (2.4ghz, 2.4ghz extended, 5ghz) at home to only one (5ghz). This way I'm reducing the exposition to radio waves for the whole familly at home.
Let's go technical

Ok, enough drama for now, let's go technical. 😁
Setup


## llm-wiki.md

      
              1 file
            
          
              209 forks
            
          
                19 comments
              
            
              1493 stars
            
          
                rohitg00
                / llm-wiki.md
            
            
              Last active
              July 1, 2026 05:43
                — forked from karpathy/llm-wiki.md
            
              
                LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory
              
          
    LLM Wiki v2

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
What the original gets right

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

  
## cloudflare.md

      
              1 file
            
          
              9 forks
            
          
                7 comments
              
            
              67 stars
            
          
                CharlesGodwin
                / cloudflare.md
            
            
              Last active
              July 1, 2026 05:42
            
              
                I Don't Need Port Forwarding and Don't Care About CGNAT
              
          
    I Don't Need Port Forwarding and Don't Care About CGNAT
This was rewritten 2022-11-30
This article is for users that want all these features:

To connect to home network from anywhere
Can connect without any port forwarding; either by choice or internet provider can't or won't provide access
No setup or configuration or installation on client machine
No enrolment / registration required


## x.sh
#!/bin/bash
# Fetch tweet or user profile in an LLM-friendly format using fxtwitter API

set -e

if [ -z "$1" ]; then
  echo "Usage: x <twitter-url-or-username>"
  echo "Examples:"
  echo "  x https://x.com/marckohlbrugge/status/2005972157445333371"
  echo "  x https://x.com/marckohlbrugge"

## deploy.yml
# Optimistic Builds for Kamal
#
# This workflow speeds up deploys by building the Docker image in parallel with CI.
# Instead of: CI → Build → Deploy (sequential)
# We do: CI ↘
#              → Deploy
#        Build ↗
#
# The "optimistic" part: we start building before knowing if tests pass.
# Since CI usually passes, this saves ~2 minutes per deploy.
	using UnityEngine;

	public class ParticleController : MonoBehaviour {
	[SerializeField] ComputeShader compute;
	[SerializeField] Mesh mesh;
	[SerializeField] Material material;
	[SerializeField] int particleCount = 10000;
	[SerializeField] float areaSize = 10f;
	[SerializeField] float spawnDistanceFromCamera = 8f;
	#!/bin/bash
	# Fetch tweet or user profile in an LLM-friendly format using fxtwitter API

	set -e

	if [ -z "$1" ]; then
	echo "Usage: x <twitter-url-or-username>"
	echo "Examples:"
	echo " x https://x.com/marckohlbrugge/status/2005972157445333371"
	echo " x https://x.com/marckohlbrugge"
	# Optimistic Builds for Kamal
	#
	# This workflow speeds up deploys by building the Docker image in parallel with CI.
	# Instead of: CI → Build → Deploy (sequential)
	# We do: CI ↘
	# → Deploy
	# Build ↗
	#
	# The "optimistic" part: we start building before knowing if tests pass.
	# Since CI usually passes, this saves ~2 minutes per deploy.